Rational analysis as a link between human memory and information retrieval

Mark Steyvers, Thomas L. Griffiths

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations


This chapter provides a complementary Bayesian analysis of the problem of memory retrieval. A Bayesian model that is able both to classify words into semantically coherent groups, merely from observing their co-occurrence patterns in texts, is used as the basis for understanding aspects not only of how some linguistic categories might be created, but also how relevant information can be retrieved, using probabilistic principles. This work can be viewed as a natural follow-on from Anderson and colleagues' pioneering rational analyses of memory (Anderson & Milson, 1989; Anderson & Schooler, 1991). This chapter uses innovations in information retrieval as a way to explore the connections between research on human memory and information retrieval systems. It also provides an example of how cognitive research can help information retrieval research by formalizing theories of knowledge and memory organization that have been proposed by cognitive psychologists.

Original languageEnglish (US)
Title of host publicationThe Probabilistic Mind
Subtitle of host publicationProspects for Bayesian cognitive science
PublisherOxford University Press
ISBN (Electronic)9780191695971
ISBN (Print)9780199216093
StatePublished - Mar 22 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Psychology


  • Bayesian analysis
  • Cognitive psychologists
  • Human memory
  • Information retrieval
  • Knowledge
  • Memory organization
  • Memory retrieval


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